Objective
To compare previously used algorithms to identify anovulatory menstrual cycles in women self-reporting regular menses.
Design
Prospective cohort study
Setting
Western New York
Study participants
259 healthy, regularly menstruating women followed for one (n=9) or two (n=250) menstrual cycles (2005–2007).
Intervention(s)
None.
Main Outcome Measure(s)
Prevalence of sporadic anovulatory cycles identified using eleven previously defined algorithms that utilize estradiol, progesterone, and luteinizing hormone (LH) concentrations.
Result(s)
Algorithms based on serum LH, estradiol, and progesterone levels detected a prevalence of anovulation across the study period of 5.5% to 12.8% (concordant classification for 91.7% to 97.4% of cycles). The prevalence of anovulatory cycles varied from 3.4% to 18.6% using algorithms based on urinary LH alone or with the primary estradiol metabolite, estrone-3-glucuronide (E3G), levels.
Conclusion(s)
The prevalence of anovulatory cycles among healthy women varied by algorithm. Mid-cycle LH surge urine-based algorithms used in over-the-counter fertility monitors tended to classify a higher proportion of anovulatory cycles compared to luteal phase progesterone serum-based algorithms. Our study demonstrates that algorithms based on the LH surge, or in conjunction with E3G, potentially estimate a higher percentage of anovulatory episodes. Addition of measurements of post-ovulatory serum progesterone or urine pregnanediol may aid in detecting ovulation.